# Image Gradient-Aided Photometric Stereo Network

arXiv: https://arxiv.org/abs/2412.11650 · HTML: https://mlsanity.com/paper/2412.11650/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 35 references that are in the map.
- First posted on arXiv 16 Dec 2024.

## Builds on (most cited first, 13 of 13)

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Pyramid Stereo Matching Network](https://mlsanity.com/paper/1803.08669/) (2018)
- [Pixel Difference Networks for Efficient Edge Detection](https://mlsanity.com/paper/2108.07009/) (2021)
- [Structure-Preserving Super Resolution with Gradient Guidance](https://mlsanity.com/paper/2003.13081/) (2020)
- [PS-FCN: A Flexible Learning Framework for Photometric Stereo](https://mlsanity.com/paper/1807.08696/) (2018)
- [CNN-PS: CNN-based Photometric Stereo for General Non-Convex Surfaces](https://mlsanity.com/paper/1808.10093/) (2018)
- [Neural Inverse Rendering for General Reflectance Photometric Stereo](https://mlsanity.com/paper/1802.10328/) (2018)
- [SilNet : Single- and Multi-View Reconstruction by Learning from Silhouettes](https://mlsanity.com/paper/1711.07888/) (2017)
- [Deep Photometric Stereo for Non-Lambertian Surfaces](https://mlsanity.com/paper/2007.13145/) (2020)
- [PX-NET: Simple and Efficient Pixel-Wise Training of Photometric Stereo Networks](https://mlsanity.com/paper/2008.04933/) (2020)
- [PS-Transformer: Learning Sparse Photometric Stereo Network using Self-Attention Mechanism](https://mlsanity.com/paper/2211.11386/) (2022)
- [Deep Learning Methods for Calibrated Photometric Stereo and Beyond](https://mlsanity.com/paper/2212.08414/) (2022)

## Built on by (most cited first, 1 of 1)

- [MSF-Net: Multi-Stage Feature Extraction and Fusion for Robust Photometric Stereo](https://mlsanity.com/paper/2510.25221/) (2025)

## Abstract (arXiv)

Photometric stereo (PS) endeavors to ascertain surface normals using shading clues from photometric images under various illuminations. Recent deep learning-based PS methods often overlook the complexity of object surfaces. These neural network models, which exclusively rely on photometric images for training, often produce blurred results in high-frequency regions characterized by local discontinuities, such as wrinkles and edges with significant gradient changes. To address this, we propose the Image Gradient-Aided Photometric Stereo Network (IGA-PSN), a dual-branch framework extracting features from both photometric images and their gradients. Furthermore, we incorporate an hourglass regression network along with supervision to regularize normal regression. Experiments on DiLiGenT benchmarks show that IGA-PSN outperforms previous methods in surface normal estimation, achieving a mean angular error of 6.46 while preserving textures and geometric shapes in complex regions.